Automatically choosing data samples for annotation

ABSTRACT

Among other things, we describe techniques for automatically selecting data samples for annotation. The techniques use bounding box prediction based on a bounding box score distribution, spatial probability density determined from bounding box sizes and positions and an ensemble score variance determined from outputs of multiple machine learning models to select data samples for annotation. In an embodiment, temporal inconsistency cues are used to select data samples for annotation. In an embodiment, digital map constraints or other map-based data are used to exclude data samples from annotation. In an exemplary application, the annotated data samples are used to train a machine learning model that outputs perception data for an autonomous vehicle application.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/796,064, filed Jan. 23, 2019, the entire contents of which areincorporated herein by reference.

FIELD OF THE INVENTION

This description relates to machine learning, and more particularly tousing active learning techniques to automatically select data samplesfor annotation.

BACKGROUND

Convolutional neural networks (CNN) have been successfully used forperception tasks in autonomous driving applications. The CNN structureincludes layers that learn by training data. Learning to classify imageand video samples requires that a human annotator label each data samplewith a label. Having to annotate thousands of data samples is tediousand redundant.

SUMMARY

An active learning system and method is disclosed for automaticallyselecting data samples (e.g., images, point clouds) from a data samplepool for a human to annotate. The system and method use bounding boxprediction based on a bounding box score probability distribution,spatial probability density determined from bounding box sizes andpositions in the data samples and ensemble score variances determinedfrom outputs of multiple machine learning models to select data samplesfor annotation. In an embodiment, temporal inconsistency cues are usedto select for annotation data samples (e.g., successive video frames)that have temporal noise (e.g., flicker). In an embodiment, digital mapsare used to exclude from annotation data samples that violate mapconstraints (hard and/or statistical constraints). In an exemplaryapplication, the annotated data samples are used to train a machinelearning model (e.g., a CNN) that outputs perception data (e.g., labeledobjects and scenes) for an autonomous vehicle application. In anembodiment, the active learning system is implemented in a planningmodule of an autonomous vehicle.

Advantages of the disclosed active learning system include automaticallyselecting a subset of data from a large sample pool for humanannotation, thus reducing the amount of time and cost in manuallypreparing training data sets for machine learning models for autonomousvehicle applications.

In an embodiment, a method comprises: obtaining, using a computersystem, a set of data samples, each data sample including one or morebounding boxes, each bounding box containing a potential object or scenein an environment, each bounding box having a label and bounding boxscore indicating a confidence in the label being correct; and selecting,using the computer system, a subset of data samples for annotation basedon a bounding box prediction confidence determined using a probabilitydistribution of bounding box scores, and an ensemble score variancebased on differences in ensemble scores computed from sets ofpredictions output by multiple machine learning models.

In an embodiment, the method further comprises: selecting, using thecomputer system, the subset of data samples for annotation based on thebounding box prediction confidence, a spatial probability density of thebounding boxes parameterized by bounding box size and position and theensemble score variance.

In an embodiment, the bounding box prediction further comprises: foreach label, generating a probability distribution of bounding boxscores; and determining, based on the distribution, a likelihood that aparticular bounding box is incorrectly labeled; and selecting orexcluding the particular bounding box for annotation based on thelikelihood.

In an embodiment, the distribution is approximated by a histogram havingbins representing ranges of bounding box scores, and each bin isassociated with a likelihood.

In an embodiment, for each bin the likelihood is calculated from a ratioof a number of incorrectly labeled bounding boxes assigned to the binand a number of labeled bounding boxes assigned to the bin.

In an embodiment, the method further comprises: for each label, sensorand scale, determining the spatial probability density using a GaussianMixture Model (GMM) over a set of bounding boxes for the label, sensorand scale, where the GMM is parameterized by bounding box size andposition.

In an embodiment, the spatial probability density for the label isdetermined by dividing the spatial densities for the label by a largestdensity value among all spatial density values for the label.

In an embodiment, the method further comprises: processing the datasamples through a plurality of different machine learning models togenerate predicted labeled bounding boxes; computing an ensemble scorefor each pairwise comparison of the predicted labeled bounding boxes,where each predicted labeled bounding box is a ground truth forcomparison with the other predicted labeled bounding boxes; andcomputing an ensemble score variance based on the ensemble scores.

In an embodiment, the plurality of different machine learning modelsincludes a plurality of different neural networks tuned by training datasamples provided by different types of sensors.

In an embodiment, the different types of sensors include LiDAR, RADARand a camera.

In an embodiment, the plurality of different neural networks are trainedon different random orders of the training data samples.

In an embodiment, the method further comprises: detecting, by thecomputer system, temporal inconsistency between successive data samples;and in accordance with temporal inconsistency being detected, selectingthe successive data samples for annotation.

In an embodiment, the method further comprises: using, by the computersystem, map constraints to detect an error associated with a boundingbox; and in accordance with the error being detected, excluding thebounding box from annotation.

These and other aspects, features, and implementations can be expressedas methods, apparatus, systems, components, program products, means orsteps for performing a function, and in other ways.

These and other aspects, features, and implementations will becomeapparent from the following descriptions, including the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example of an autonomous vehicle having autonomouscapability.

FIG. 2 illustrates an example “cloud” computing environment.

FIG. 3 illustrates a computer system.

FIG. 4 shows an example architecture for an autonomous vehicle.

FIG. 5 shows an example of inputs and outputs that may be used by aperception module.

FIG. 6 shows an example of a LiDAR system.

FIG. 7 shows the LiDAR system in operation.

FIG. 8 shows the operation of the LiDAR system in additional detail.

FIG. 9 shows a block diagram of the relationships between inputs andoutputs of a planning module.

FIG. 10 shows a directed graph used in path planning.

FIG. 11 shows a block diagram of the inputs and outputs of a controlmodule.

FIG. 12 shows a block diagram of the inputs, outputs, and components ofa controller.

FIG. 13 shows a scene output by a perception module that includesbounding boxes and corresponding labels and bounding box scores.

FIG. 14 shows a block diagram of an active learning system forautomatically selecting data samples for annotation.

FIG. 15 shows a stacked histogram of bounding box scores.

FIG. 16 shows a plot of spatial GMM density for a particular label and aparticular sensor.

FIG. 17 shows a block diagram of an ensemble system.

FIG. 18 shows a flow diagram of an active learning process forautomatically selecting samples for annotation.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to avoid unnecessarily obscuring thepresent invention.

In the drawings, specific arrangements or orderings of schematicelements, such as those representing devices, modules, instructionblocks and data elements, are shown for ease of description. However, itshould be understood by those skilled in the art that the specificordering or arrangement of the schematic elements in the drawings is notmeant to imply that a particular order or sequence of processing, orseparation of processes, is required. Further, the inclusion of aschematic element in a drawing is not meant to imply that such elementis required in all embodiments or that the features represented by suchelement may not be included in or combined with other elements in someembodiments.

Further, in the drawings, where connecting elements, such as solid ordashed lines or arrows, are used to illustrate a connection,relationship, or association between or among two or more otherschematic elements, the absence of any such connecting elements is notmeant to imply that no connection, relationship, or association canexist. In other words, some connections, relationships, or associationsbetween elements are not shown in the drawings so as not to obscure thedisclosure. In addition, for ease of illustration, a single connectingelement is used to represent multiple connections, relationships orassociations between elements. For example, where a connecting elementrepresents a communication of signals, data, or instructions, it shouldbe understood by those skilled in the art that such element representsone or multiple signal paths (e.g., a bus), as may be needed, to affectthe communication.

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the various described embodiments. However,it will be apparent to one of ordinary skill in the art that the variousdescribed embodiments may be practiced without these specific details.In other instances, well-known methods, procedures, components,circuits, and networks have not been described in detail so as not tounnecessarily obscure aspects of the embodiments.

Several features are described hereafter that can each be usedindependently of one another or with any combination of other features.However, any individual feature may not address any of the problemsdiscussed above or might only address one of the problems discussedabove. Some of the problems discussed above might not be fully addressedby any of the features described herein. Although headings are provided,information related to a particular heading, but not found in thesection having that heading, may also be found elsewhere in thisdescription. Embodiments are described herein according to the followingoutline:

1. General Overview

2. System Overview

3. Autonomous Vehicle Architecture

4. Autonomous Vehicle Inputs

5. Autonomous Vehicle Planning

6. Autonomous Vehicle Control

7. Active Learning System

General Overview

Among other things, we describe techniques for automatically selectingdata samples (e.g., images, point clouds) for a human to annotate. Thetechniques use bounding box predictions, spatial probability densitiesdetermined from bounding box sizes and positions and an ensemble scorevariance determined from outputs of multiple machine learning models, toselect data samples for annotation. In an embodiment, temporalinconsistency cues are used to select data samples for annotation. In anembodiment, digital map constraints or other map-based data are used toexclude data samples from annotation. In an exemplary application, theannotated data samples are used to train a machine learning model (e.g.,a CNN) that outputs perception data (e.g., labeled objects and scenes)for an autonomous vehicle application. In an embodiment, the activelearning system can be implemented by a planning module of an autonomousvehicle.

System Overview

FIG. 1 shows an example of an autonomous vehicle 100 having autonomouscapability.

As used herein, the term “autonomous capability” refers to a function,feature, or facility that enables a vehicle to be partially or fullyoperated without real-time human intervention, including withoutlimitation fully autonomous vehicles, highly autonomous vehicles, andconditionally autonomous vehicles.

As used herein, an autonomous vehicle (AV) is a vehicle that possessesautonomous capability.

As used herein, “vehicle” includes means of transportation of goods orpeople. For example, cars, buses, trains, airplanes, drones, trucks,boats, ships, submersibles, dirigibles, etc. A driverless car is anexample of a vehicle.

As used herein, “trajectory” refers to a path or route to navigate an AVfrom a first spatiotemporal location to second spatiotemporal location.In an embodiment, the first spatiotemporal location is referred to asthe initial or starting location and the second spatiotemporal locationis referred to as the destination, final location, goal, goal position,or goal location. In some examples, a trajectory is made up of one ormore segments (e.g., sections of road) and each segment is made up ofone or more blocks (e.g., portions of a lane or intersection). In anembodiment, the spatiotemporal locations correspond to real worldlocations. For example, the spatiotemporal locations are pick up ordrop-off locations to pick up or drop-off persons or goods.

As used herein, “sensor(s)” includes one or more hardware componentsthat detect information about the environment surrounding the sensor.Some of the hardware components can include sensing components (e.g.,image sensors, biometric sensors), transmitting and/or receivingcomponents (e.g., laser or radio frequency wave transmitters andreceivers), electronic components such as analog-to-digital converters,a data storage device (such as a RAM and/or a nonvolatile storage),software or firmware components and data processing components such asan ASIC (application-specific integrated circuit), a microprocessorand/or a microcontroller.

As used herein, a “scene description” is a data structure (e.g., list)or data stream that includes one or more classified or labeled objectsdetected by one or more sensors on the AV vehicle or provided by asource external to the AV.

As used herein, a “road” is a physical area that can be traversed by avehicle, and may correspond to a named thoroughfare (e.g., city street,interstate freeway, etc.) or may correspond to an unnamed thoroughfare(e.g., a driveway in a house or office building, a section of a parkinglot, a section of a vacant lot, a dirt path in a rural area, etc.).Because some vehicles (e.g., 4-wheel-drive pickup trucks, sport utilityvehicles, etc.) are capable of traversing a variety of physical areasnot specifically adapted for vehicle travel, a “road” may be a physicalarea not formally defined as a thoroughfare by any municipality or othergovernmental or administrative body.

As used herein, a “lane” is a portion of a road that can be traversed bya vehicle. A lane is sometimes identified based on lane markings. Forexample, a lane may correspond to most or all of the space between lanemarkings, or may correspond to only some (e.g., less than 50%) of thespace between lane markings. For example, a road having lane markingsspaced far apart might accommodate two or more vehicles between themarkings, such that one vehicle can pass the other without traversingthe lane markings, and thus could be interpreted as having a lanenarrower than the space between the lane markings, or having two lanesbetween the lane markings. A lane could also be interpreted in theabsence of lane markings. For example, a lane may be defined based onphysical features of an environment, e.g., rocks and trees along athoroughfare in a rural area or, e.g., natural obstructions to beavoided in an undeveloped area. A lane could also be interpretedindependent of lane markings or physical features. For example, a lanecould be interpreted based on an arbitrary path free of obstructions inan area that otherwise lacks features that would be interpreted as laneboundaries. In an example scenario, an AV could interpret a lane throughan obstruction-free portion of a field or empty lot. In another examplescenario, an AV could interpret a lane through a wide (e.g., wide enoughfor two or more lanes) road that does not have lane markings. In thisscenario, the AV could communicate information about the lane to otherAVs so that the other AVs can use the same lane information tocoordinate path planning among themselves.

The term “over-the-air (OTA) client” includes any AV, or any electronicdevice (e.g., computer, controller, IoT device, electronic control unit(ECU)) that is embedded in, coupled to, or in communication with an AV.

The term “over-the-air (OTA) update” means any update, change, deletionor addition to software, firmware, data or configuration settings, orany combination thereof, that is delivered to an OTA client usingproprietary and/or standardized wireless communications technology,including but not limited to: cellular mobile communications (e.g., 2G,3G, 4G, 5G), radio wireless area networks (e.g., WiFi) and/or satelliteInternet.

The term “edge node” means one or more edge devices coupled to a networkthat provide a portal for communication with AVs and can communicatewith other edge nodes and a cloud based computing platform, forscheduling and delivering OTA updates to OTA clients.

The term “edge device” means a device that implements an edge node andprovides a physical wireless access point (AP) into enterprise orservice provider (e.g., Verizon□, AT&T□) core networks. Examples of edgedevices include but are not limited to: computers, controllers,transmitters, routers, routing switches, integrated access devices(IADs), multiplexers, metropolitan area network (MAN) and wide areanetwork (WAN) access devices.

“One or more” includes a function being performed by one element, afunction being performed by more than one element, e.g., in adistributed fashion, several functions being performed by one element,several functions being performed by several elements, or anycombination of the above.

It will also be understood that, although the terms first, second, etc.are, in some instances, used herein to describe various elements, theseelements should not be limited by these terms. These terms are only usedto distinguish one element from another. For example, a first contactcould be termed a second contact, and, similarly, a second contact couldbe termed a first contact, without departing from the scope of thevarious described embodiments. The first contact and the second contactare both contacts, but they are not the same contact.

The terminology used in the description of the various describedembodiments herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used in thedescription of the various described embodiments and the appendedclaims, the singular forms “a,” “an” and “the” are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. It will also be understood that the term “and/or” as usedherein refers to and encompasses any and all possible combinations ofone or more of the associated listed items. It will be furtherunderstood that the terms “includes,” “including,” “comprises,” and/or“comprising,” when used in this description, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

As used herein, the term “if” is, optionally, construed to mean “when”or “upon” or “in response to determining” or “in response to detecting,”depending on the context. Similarly, the phrase “if it is determined” or“if [a stated condition or event] is detected” is, optionally, construedto mean “upon determining” or “in response to determining” or “upondetecting [the stated condition or event]” or “in response to detecting[the stated condition or event],” depending on the context.

As used herein, an AV system refers to the AV along with the array ofhardware, software, stored data, and data generated in real-time thatsupports the operation of the AV. In an embodiment, the AV system isincorporated within the AV. In an embodiment, the AV system is spreadacross several locations. For example, some of the software of the AVsystem is implemented on a cloud computing environment similar to cloudcomputing environment 300 described below with respect to FIG. 3 .

In general, this document describes technologies applicable to anyvehicles that have one or more autonomous capabilities including fullyautonomous vehicles, highly autonomous vehicles, and conditionallyautonomous vehicles, such as so-called Level 5, Level 4 and Level 3vehicles, respectively (see SAE International's standard J3016: Taxonomyand Definitions for Terms Related to On-Road Motor Vehicle AutomatedDriving Systems, which is incorporated by reference in its entirety, formore details on the classification of levels of autonomy in vehicles).The technologies described in this document are also applicable topartially autonomous vehicles and driver assisted vehicles, such asso-called Level 2 and Level 1 vehicles (see SAE International's standardJ3016: Taxonomy and Definitions for Terms Related to On-Road MotorVehicle Automated Driving Systems). In an embodiment, one or more of theLevel 1, 2, 3, 4 and 5 vehicle systems may automate certain vehicleoperations (e.g., steering, braking, and using maps) under certainoperating conditions based on processing of sensor inputs. Thetechnologies described in this document can benefit vehicles in anylevels, ranging from fully autonomous vehicles to human-operatedvehicles.

Autonomous vehicles have advantages over vehicles that require a humandriver. One advantage is safety. For example, in 2016, the United Statesexperienced 6 million automobile accidents, 2.4 million injuries, 40,000fatalities, and 13 million vehicles in crashes, estimated at a societalcost of $910+ billion. U.S. traffic fatalities per 100 million milestraveled have been reduced from about six to about one from 1965 to2015, in part due to additional safety measures deployed in vehicles.For example, an additional half second of warning that a crash is aboutto occur is believed to mitigate 60% of front-to-rear crashes. However,passive safety features (e.g., seat belts, airbags) have likely reachedtheir limit in improving this number. Thus, active safety measures, suchas automated control of a vehicle, are the likely next step in improvingthese statistics. Because human drivers are believed to be responsiblefor a critical pre-crash event in 95% of crashes, automated drivingsystems are likely to achieve better safety outcomes, e.g., by reliablyrecognizing and avoiding critical situations better than humans; makingbetter decisions, obeying traffic laws, and predicting future eventsbetter than humans; and reliably controlling a vehicle better than ahuman.

Referring to FIG. 1 , an AV system 120 operates the AV 100 along atrajectory 198 through an environment 190 to a destination 199(sometimes referred to as a final location) while avoiding objects(e.g., natural obstructions 191, vehicles 193, pedestrians 192,cyclists, and other obstacles) and obeying rules of the road (e.g.,rules of operation or driving preferences).

In an embodiment, the AV system 120 includes devices 101 that areinstrumented to receive and act on operational commands from thecomputer processors 146. In an embodiment, computing processors 146 aresimilar to the processor 304 described below in reference to FIG. 3 .Examples of devices 101 include a steering control 102, brakes 103,gears, accelerator pedal or other acceleration control mechanisms,windshield wipers, side-door locks, window controls, andturn-indicators.

In an embodiment, the AV system 120 includes sensors 121 for measuringor inferring properties of state or condition of the AV 100, such as theAV's position, linear and angular velocity and acceleration, and heading(e.g., an orientation of the leading end of AV 100). Example of sensors121 are GPS, inertial measurement units (IMU) that measure both vehiclelinear accelerations and angular rates, wheel speed sensors formeasuring or estimating wheel slip ratios, wheel brake pressure orbraking torque sensors, engine torque or wheel torque sensors, andsteering angle and angular rate sensors.

In an embodiment, the sensors 121 also include sensors for sensing ormeasuring properties of the AV's environment. For example, monocular orstereo video cameras 122 in the visible light, infrared or thermal (orboth) spectra, LiDAR 123, RADAR, ultrasonic sensors, time-of-flight(TOF) depth sensors, speed sensors, temperature sensors, humiditysensors, and precipitation sensors.

In an embodiment, the AV system 120 includes a data storage unit 142 andmemory 144 for storing machine instructions associated with computerprocessors 146 or data collected by sensors 121. In an embodiment, thedata storage unit 142 is similar to the ROM 308 or storage device 310described below in relation to FIG. 3 . In an embodiment, memory 144 issimilar to the main memory 306 described below. In an embodiment, thedata storage unit 142 and memory 144 store historical, real-time, and/orpredictive information about the environment 190. In an embodiment, thestored information includes maps, driving performance, trafficcongestion updates or weather conditions. In an embodiment, datarelating to the environment 190 is transmitted to the AV 100 via acommunications channel from a remotely located database 134.

In an embodiment, the AV system 120 includes communications devices 140for communicating measured or inferred properties of other vehicles'states and conditions, such as positions, linear and angular velocities,linear and angular accelerations, and linear and angular headings to theAV 100. These devices include Vehicle-to-Vehicle (V2V) andVehicle-to-Infrastructure (V2I) communication devices and devices forwireless communications over point-to-point or ad hoc networks or both.In an embodiment, the communications devices 140 communicate across theelectromagnetic spectrum (including radio and optical communications) orother media (e.g., air and acoustic media). A combination ofVehicle-to-Vehicle (V2V) Vehicle-to-Infrastructure (V2I) communication(and, in some embodiments, one or more other types of communication) issometimes referred to as Vehicle-to-Everything (V2X) communication. V2Xcommunication typically conforms to one or more communications standardsfor communication with, between, and among autonomous vehicles.

In an embodiment, the communication devices 140 include communicationinterfaces. For example, wired, wireless, WiMAX, WiFi, Bluetooth,satellite, cellular, optical, near field, infrared, or radio interfaces.The communication interfaces transmit data from a remotely locateddatabase 134 to AV system 120. In an embodiment, the remotely locateddatabase 134 is embedded in a cloud computing environment 200 asdescribed in FIG. 2 . The communication interfaces 140 transmit datacollected from sensors 121 or other data related to the operation of AV100 to the remotely located database 134. In an embodiment,communication interfaces 140 transmit information that relates toteleoperations to the AV 100. In some embodiments, the AV 100communicates with other remote (e.g., “cloud”) servers 136.

In an embodiment, the remotely located database 134 also stores andtransmits digital data (e.g., storing data such as road and streetlocations). Such data is stored on the memory 144 on the AV 100, ortransmitted to the AV 100 via a communications channel from the remotelylocated database 134.

In an embodiment, the remotely located database 134 stores and transmitshistorical information about driving properties (e.g., speed andacceleration profiles) of vehicles that have previously traveled alongtrajectory 198 at similar times of day. In one implementation, such datamay be stored on the memory 144 on the AV 100, or transmitted to the AV100 via a communications channel from the remotely located database 134.

Computing devices 146 located on the AV 100 algorithmically generatecontrol actions based on both real-time sensor data and priorinformation, allowing the AV system 120 to execute its autonomousdriving capabilities.

In an embodiment, the AV system 120 includes computer peripherals 132coupled to computing devices 146 for providing information and alertsto, and receiving input from, a user (e.g., an occupant or a remoteuser) of the AV 100. In an embodiment, peripherals 132 are similar tothe display 312, input device 314, and cursor controller 316 discussedbelow in reference to FIG. 3 . The coupling is wireless or wired. Anytwo or more of the interface devices may be integrated into a singledevice.

FIG. 2 illustrates an example “cloud” computing environment. Cloudcomputing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services). Intypical cloud computing systems, one or more large cloud data centershouse the machines used to deliver the services provided by the cloud.Referring now to FIG. 2 , the cloud computing environment 200 includescloud data centers 204 a, 204 b, and 204 c that are interconnectedthrough the cloud 202. Data centers 204 a, 204 b, and 204 c providecloud computing services to computer systems 206 a, 206 b, 206 c, 206 d,206 e, and 206 f connected to cloud 202.

The cloud computing environment 200 includes one or more cloud datacenters. In general, a cloud data center, for example the cloud datacenter 204 a shown in FIG. 2 , refers to the physical arrangement ofservers that make up a cloud, for example the cloud 202 shown in FIG. 2, or a particular portion of a cloud. For example, servers arephysically arranged in the cloud datacenter into rooms, groups, rows,and racks. A cloud datacenter has one or more zones, which include oneor more rooms of servers. Each room has one or more rows of servers, andeach row includes one or more racks. Each rack includes one or moreindividual server nodes. In some implementation, servers in zones,rooms, racks, and/or rows are arranged into groups based on physicalinfrastructure requirements of the datacenter facility, which includepower, energy, thermal, heat, and/or other requirements. In anembodiment, the server nodes are similar to the computer systemdescribed in FIG. 3 . The data center 204 a has many computing systemsdistributed through many racks.

The cloud 202 includes cloud data centers 204 a, 204 b, and 204 c alongwith the network and networking resources (for example, networkingequipment, nodes, routers, switches, and networking cables) thatinterconnect the cloud data centers 204 a, 204 b, and 204 c and helpfacilitate the computing systems' 206 a-f access to cloud computingservices. In an embodiment, the network represents any combination ofone or more local networks, wide area networks, or internetworks coupledusing wired or wireless links deployed using terrestrial or satelliteconnections. Data exchanged over the network, is transferred using anynumber of network layer protocols, such as Internet Protocol (IP),Multiprotocol Label Switching (MPLS), Asynchronous Transfer Mode (ATM),Frame Relay, etc. Furthermore, in embodiments where the networkrepresents a combination of multiple sub-networks, different networklayer protocols are used at each of the underlying sub-networks. In someembodiments, the network represents one or more interconnectedinternetworks, such as the public Internet.

The computing systems 206 a-f or cloud computing services consumers areconnected to the cloud 202 through network links and network adapters.In an embodiment, the computing systems 206 a-f are implemented asvarious computing devices, for example servers, desktops, laptops,tablet, smartphones, Internet of Things (IoT) devices, autonomousvehicles (including, cars, drones, shuttles, trains, buses, etc.) andconsumer electronics. In an embodiment, the computing systems 206 a-fare implemented in or as a part of other systems.

FIG. 3 illustrates a computer system 300. In an implementation, thecomputer system 300 is a special purpose computing device. Thespecial-purpose computing device is hard-wired to perform the techniquesor includes digital electronic devices such as one or moreapplication-specific integrated circuits (ASICs) or field programmablegate arrays (FPGAs) that are persistently programmed to perform thetechniques, or may include one or more general purpose hardwareprocessors programmed to perform the techniques pursuant to programinstructions in firmware, memory, other storage, or a combination. Suchspecial-purpose computing devices may also combine custom hard-wiredlogic, ASICs, or FPGAs with custom programming to accomplish thetechniques. In various embodiments, the special-purpose computingdevices are desktop computer systems, portable computer systems,handheld devices, network devices or any other device that incorporateshard-wired and/or program logic to implement the techniques.

In an embodiment, the computer system 300 includes a bus 302 or othercommunication mechanism for communicating information, and a hardwareprocessor 304 coupled with a bus 302 for processing information. Thehardware processor 304 is, for example, a general-purposemicroprocessor. The computer system 300 also includes a main memory 306,such as a random-access memory (RAM) or other dynamic storage device,coupled to the bus 302 for storing information and instructions to beexecuted by processor 304. In one implementation, the main memory 306 isused for storing temporary variables or other intermediate informationduring execution of instructions to be executed by the processor 304.Such instructions, when stored in non-transitory storage mediaaccessible to the processor 304, render the computer system 300 into aspecial-purpose machine that is customized to perform the operationsspecified in the instructions.

In an embodiment, the computer system 300 further includes a read onlymemory (ROM) 308 or other static storage device coupled to the bus 302for storing static information and instructions for the processor 304. Astorage device 310, such as a magnetic disk, optical disk, solid-statedrive, or three-dimensional cross point memory is provided and coupledto the bus 302 for storing information and instructions.

In an embodiment, the computer system 300 is coupled via the bus 302 toa display 312, such as a cathode ray tube (CRT), a liquid crystaldisplay (LCD), plasma display, light emitting diode (LED) display, or anorganic light emitting diode (OLED) display for displaying informationto a computer user. An input device 314, including alphanumeric andother keys, is coupled to bus 302 for communicating information andcommand selections to the processor 304. Another type of user inputdevice is a cursor controller 316, such as a mouse, a trackball, atouch-enabled display, or cursor direction keys for communicatingdirection information and command selections to the processor 304 andfor controlling cursor movement on the display 312. This input devicetypically has two degrees of freedom in two axes, a first axis (e.g.,x-axis) and a second axis (e.g., y-axis), that allows the device tospecify positions in a plane.

According to one embodiment, the techniques herein are performed by thecomputer system 300 in response to the processor 304 executing one ormore sequences of one or more instructions contained in the main memory306. Such instructions are read into the main memory 306 from anotherstorage medium, such as the storage device 310. Execution of thesequences of instructions contained in the main memory 306 causes theprocessor 304 to perform the process steps described herein. Inalternative embodiments, hard-wired circuitry is used in place of or incombination with software instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media includes non-volatilemedia and/or volatile media. Non-volatile media includes, for example,optical disks, magnetic disks, solid-state drives, or three-dimensionalcross point memory, such as the storage device 310. Volatile mediaincludes dynamic memory, such as the main memory 306. Common forms ofstorage media include, for example, a floppy disk, a flexible disk, harddisk, solid-state drive, magnetic tape, or any other magnetic datastorage medium, a CD-ROM, any other optical data storage medium, anyphysical medium with patterns of holes, a RAM, a PROM, and EPROM, aFLASH-EPROM, NV-RAM, or any other memory chip or cartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise the bus 302. Transmission media can also take theform of acoustic or light waves, such as those generated duringradio-wave and infrared data communications.

In an embodiment, various forms of media are involved in carrying one ormore sequences of one or more instructions to the processor 304 forexecution. For example, the instructions are initially carried on amagnetic disk or solid-state drive of a remote computer. The remotecomputer loads the instructions into its dynamic memory and send theinstructions over a telephone line using a modem. A modem local to thecomputer system 300 receives the data on the telephone line and use aninfrared transmitter to convert the data to an infrared signal. Aninfrared detector receives the data carried in the infrared signal andappropriate circuitry places the data on the bus 302. The bus 302carries the data to the main memory 306, from which processor 304retrieves and executes the instructions. The instructions received bythe main memory 306 may optionally be stored on the storage device 310either before or after execution by processor 304.

The computer system 300 also includes a communication interface 318coupled to the bus 302. The communication interface 318 provides atwo-way data communication coupling to a network link 320 that isconnected to a local network 322. For example, the communicationinterface 318 is an integrated service digital network (ISDN) card,cable modem, satellite modem, or a modem to provide a data communicationconnection to a corresponding type of telephone line. As anotherexample, the communication interface 318 is a local area network (LAN)card to provide a data communication connection to a compatible LAN. Insome implementations, wireless links are also implemented. In any suchimplementation, the communication interface 318 sends and receiveselectrical, electromagnetic, or optical signals that carry digital datastreams representing various types of information.

The network link 320 typically provides data communication through oneor more networks to other data devices. For example, the network link320 provides a connection through the local network 322 to a hostcomputer 324 or to a cloud data center or equipment operated by anInternet Service Provider (ISP) 326. The ISP 326 in turn provides datacommunication services through the world-wide packet data communicationnetwork now commonly referred to as the “Internet” 328. The localnetwork 322 and Internet 328 both use electrical, electromagnetic oroptical signals that carry digital data streams. The signals through thevarious networks and the signals on the network link 320 and through thecommunication interface 318, which carry the digital data to and fromthe computer system 300, are example forms of transmission media. In anembodiment, the network 320 contains the cloud 202 or a part of thecloud 202 described above.

The computer system 300 sends messages and receives data, includingprogram code, through the network(s), the network link 320, and thecommunication interface 318. In an embodiment, the computer system 300receives code for processing. The received code is executed by theprocessor 304 as it is received, and/or stored in storage device 310, orother non-volatile storage for later execution.

Autonomous Vehicle Architecture

FIG. 4 shows an example architecture 400 for an autonomous vehicle(e.g., the AV 100 shown in FIG. 1 ). The architecture 400 includes aperception module 402 (sometimes referred to as a perception circuit), aplanning module 404 (sometimes referred to as a planning circuit), acontrol module 406 (sometimes referred to as a control circuit), alocalization module 408 (sometimes referred to as a localizationcircuit), and a database module 410 (sometimes referred to as a databasecircuit). Each module plays a role in the operation of the AV 100.Together, the modules 402, 404, 406, 408, and 410 may be part of the AVsystem 120 shown in FIG. 1 . In some embodiments, any of the modules402, 404, 406, 408, and 410 is a combination of computer software (e.g.,executable code stored on a computer-readable medium) and computerhardware (e.g., one or more microprocessors, microcontrollers,application-specific integrated circuits [ASICs]), hardware memorydevices, other types of integrated circuits, other types of computerhardware, or a combination of any or all of these things). Each of themodules 402, 404, 406, 408, and 410 is sometimes referred to as aprocessing circuit (e.g., computer hardware, computer software, or acombination of the two). A combination of any or all of the modules 402,404, 406, 408, and 410 is also an example of a processing circuit.

In use, the planning module 404 receives data representing a destination412 and determines data representing a trajectory 414 (sometimesreferred to as a route) that can be traveled by the AV 100 to reach(e.g., arrive at) the destination 412. In order for the planning module404 to determine the data representing the trajectory 414, the planningmodule 404 receives data from the perception module 402, thelocalization module 408, and the database module 410.

The perception module 402 identifies nearby physical objects using oneor more sensors 121, e.g., as also shown in FIG. 1 . The objects areclassified (e.g., grouped into types such as pedestrian, bicycle,automobile, traffic sign, etc.) and a scene description including theclassified objects 416 is provided to the planning module 404. In anembodiment, the perception module 402 includes an object detector thatdetects and labels objects. An example object detector is aconvolutional neural network (CNN). In an embodiment, the objectdetector can output an image or point cloud that includes bounding boxessurrounding the detected objects, labels for the objects and boundingbox scores that indicate a degree of confidence that the objectsdetected were correctly labeled. In an embodiment, the bounding boxscore can be in the range [0.0 1.0], where 0.0 indicates an incorrectlylabeled object, 1.0 indicates a correctly labeled object and values inbetween indicate a degree of confidence that the object was correctlylabeled.

The planning module 404 also receives data representing the AV position418 from the localization module 408. The localization module 408determines the AV position by using data from the sensors 121 and datafrom the database module 410 (e.g., a geographic data) to calculate aposition. For example, the localization module 408 uses data from a GNSS(Global Navigation Satellite System) sensor and geographic data tocalculate a longitude and latitude of the AV. In an embodiment, dataused by the localization module 408 includes high-precision maps of theroadway geometric properties, maps describing road network connectivityproperties, maps describing roadway physical properties (such as trafficspeed, traffic volume, the number of vehicular and cyclist trafficlanes, lane width, lane traffic directions, or lane marker types andlocations, or combinations of them), and maps describing the spatiallocations of road features such as crosswalks, traffic signs or othertravel signals of various types. In an embodiment, the high-precisionmaps are constructed by adding data through automatic or manualannotation to low-precision maps.

The control module 406 receives the data representing the trajectory 414and the data representing the AV position 418 and operates the controlfunctions 420 a-c (e.g., steering, throttling, braking, ignition) of theAV in a manner that will cause the AV 100 to travel the trajectory 414to the destination 412. For example, if the trajectory 414 includes aleft turn, the control module 406 will operate the control functions 420a-c in a manner such that the steering angle of the steering functionwill cause the AV 100 to turn left and the throttling and braking willcause the AV 100 to pause and wait for passing pedestrians or vehiclesbefore the turn is made.

Autonomous Vehicle Inputs

FIG. 5 shows an example of inputs 502 a-d (e.g., sensors 121 shown inFIG. 1 ) and outputs 504 a-d (e.g., sensor data) that is used by theperception module 402 (FIG. 4 ). One input 502 a is a LiDAR (LightDetection and Ranging) system (e.g., LiDAR 123 shown in FIG. 1 ). LiDARis a technology that uses light (e.g., bursts of light such as infraredlight) to obtain data about physical objects in its line of sight. ALiDAR system produces LiDAR data as output 504 a. For example, LiDARdata is collections of 3D or 2D points (also known as a point clouds)that are used to construct a representation of the environment 190.

Another input 502 b is a RADAR system. RADAR is a technology that usesradio waves to obtain data about nearby physical objects. RADARs canobtain data about objects not within the line of sight of a LiDARsystem. A RADAR system 502 b produces RADAR data as output 504 b. Forexample, RADAR data are one or more radio frequency electromagneticsignals that are used to construct a representation of the environment190.

Another input 502 c is a camera system. A camera system uses one or morecameras (e.g., digital cameras using a light sensor such as acharge-coupled device [CCD]) to obtain information about nearby physicalobjects. A camera system produces camera data as output 504 c. Cameradata often takes the form of image data (e.g., data in an image dataformat such as RAW, JPEG, PNG, etc.). In some examples, the camerasystem has multiple independent cameras, e.g., for the purpose ofstereopsis (stereo vision), which enables the camera system to perceivedepth. Although the objects perceived by the camera system are describedhere as “nearby,” this is relative to the AV. In use, the camera systemmay be configured to “see” objects far, e.g., up to a kilometer or moreahead of the AV. Accordingly, the camera system may have features suchas sensors and lenses that are optimized for perceiving objects that arefar away.

Another input 502 d is a traffic light detection (TLD) system. A TLDsystem uses one or more cameras to obtain information about trafficlights, street signs, and other physical objects that provide visualnavigation information. A TLD system produces TLD data as output 504 d.TLD data often takes the form of image data (e.g., data in an image dataformat such as RAW, JPEG, PNG, etc.). A TLD system differs from a systemincorporating a camera in that a TLD system uses a camera with a widefield of view (e.g., using a wide-angle lens or a fish-eye lens) inorder to obtain information about as many physical objects providingvisual navigation information as possible, so that the AV 100 has accessto all relevant navigation information provided by these objects. Forexample, the viewing angle of the TLD system may be about 120 degrees ormore.

In some embodiments, outputs 504 a-d are combined using a sensor fusiontechnique. Thus, either the individual outputs 504 a-d are provided toother systems of the AV 100 (e.g., provided to a planning module 404 asshown in FIG. 4 ), or the combined output can be provided to the othersystems, either in the form of a single combined output or multiplecombined outputs of the same type (e.g., using the same combinationtechnique or combining the same outputs or both) or different types type(e.g., using different respective combination techniques or combiningdifferent respective outputs or both). In some embodiments, an earlyfusion technique is used. An early fusion technique is characterized bycombining outputs before one or more data processing steps are appliedto the combined output. In some embodiments, a late fusion technique isused. A late fusion technique is characterized by combining outputsafter one or more data processing steps are applied to the individualoutputs.

FIG. 6 shows an example of a LiDAR system 602 (e.g., the input 502 ashown in FIG. 5 ). The LiDAR system 602 emits light 604 a-c from a lightemitter 606 (e.g., a laser transmitter). Light emitted by a LiDAR systemis typically not in the visible spectrum; for example, infrared light isoften used. Some of the light 604 b emitted encounters a physical object608 (e.g., a vehicle) and reflects back to the LiDAR system 602. (Lightemitted from a LiDAR system typically does not penetrate physicalobjects, e.g., physical objects in solid form.) The LiDAR system 602also has one or more light detectors 610, which detect the reflectedlight. In an embodiment, one or more data processing systems associatedwith the LiDAR system generates an image 612 representing the field ofview 614 of the LiDAR system. The image 612 includes information thatrepresents the boundaries 616 of a physical object 608. In this way, theimage 612 is used to determine the boundaries 616 of one or morephysical objects near an AV.

FIG. 7 shows the LiDAR system 602 in operation. In the scenario shown inthis figure, the AV 100 receives both camera system output 504 c in theform of an image 702 and LiDAR system output 504 a in the form of LiDARdata points 704. In use, the data processing systems of the AV 100compares the image 702 to the data points 704. In particular, a physicalobject 706 identified in the image 702 is also identified among the datapoints 704. In this way, the AV 100 perceives the boundaries of thephysical object based on the contour and density of the data points 704.

FIG. 8 shows the operation of the LiDAR system 602 in additional detail.As described above, the AV 100 detects the boundary of a physical objectbased on characteristics of the data points detected by the LiDAR system602. As shown in FIG. 8 , a flat object, such as the ground 802, willreflect light 804 a-d emitted from a LiDAR system 602 in a consistentmanner. Put another way, because the LiDAR system 602 emits light usingconsistent spacing, the ground 802 will reflect light back to the LiDARsystem 602 with the same consistent spacing. As the AV 100 travels overthe ground 802, the LiDAR system 602 will continue to detect lightreflected by the next valid ground point 806 if nothing is obstructingthe road. However, if an object 808 obstructs the road, light 804 e-femitted by the LiDAR system 602 will be reflected from points 810 a-b ina manner inconsistent with the expected consistent manner. From thisinformation, the AV 100 can determine that the object 808 is present.

Path Planning

FIG. 9 shows a block diagram 900 of the relationships between inputs andoutputs of a planning module 404 (e.g., as shown in FIG. 4 ). Ingeneral, the output of a planning module 404 is a route 902 from a startpoint 904 (e.g., source location or initial location), and an end point906 (e.g., destination or final location). The route 902 is typicallydefined by one or more segments. For example, a segment is a distance tobe traveled over at least a portion of a street, road, highway,driveway, or other physical area appropriate for automobile travel. Insome examples, e.g., if the AV 100 is an off-road capable vehicle suchas a four-wheel-drive (4WD) or all-wheel-drive (AWD) car, SUV, pick-uptruck, or the like, the route 902 includes “off-road” segments such asunpaved paths or open fields.

In addition to the route 902, a planning module also outputs lane-levelroute planning data 908. The lane-level route planning data 908 is usedto traverse segments of the route 902 based on conditions of the segmentat a particular time. For example, if the route 902 includes amulti-lane highway, the lane-level route planning data 908 includestrajectory planning data 910 that the AV 100 can use to choose a laneamong the multiple lanes, e.g., based on whether an exit is approaching,whether one or more of the lanes have other vehicles, or other factorsthat vary over the course of a few minutes or less. Similarly, in someimplementations, the lane-level route planning data 908 includes speedconstraints 912 specific to a segment of the route 902. For example, ifthe segment includes pedestrians or un-expected traffic, the speedconstraints 912 may limit the AV 100 to a travel speed slower than anexpected speed, e.g., a speed based on speed limit data for the segment.

In an embodiment, the inputs to the planning module 404 includesdatabase data 914 (e.g., from the database module 410 shown in FIG. 4 ),current location data 916 (e.g., the AV position 418 shown in FIG. 4 ),destination data 918 (e.g., for the destination 412 shown in FIG. 4 ),and object data 920 (e.g., the classified objects 416 as perceived bythe perception module 402 as shown in FIG. 4 ). In some embodiments, thedatabase data 914 includes rules used in planning. Rules are specifiedusing a formal language, e.g., using Boolean logic. In any givensituation encountered by the AV 100, at least some of the rules willapply to the situation. A rule applies to a given situation if the rulehas conditions that are met based on information available to the AV100, e.g., information about the surrounding environment. Rules can havepriority. For example, a rule that says, “if the road is a freeway, moveto the leftmost lane” can have a lower priority than “if the exit isapproaching within a mile, move to the rightmost lane.”

FIG. 10 shows a directed graph 1000 used in path planning, e.g., by theplanning module 404 (FIG. 4 ). In general, a directed graph 1000 likethe one shown in FIG. 10 is used to determine a path between any startpoint 1002 and end point 1004. In real-world terms, the distanceseparating the start point 1002 and end point 1004 may be relativelylarge (e.g., in two different metropolitan areas) or may be relativelysmall (e.g., two intersections abutting a city block or two lanes of amulti-lane road).

In an embodiment, the directed graph 1000 has nodes 1006 a-drepresenting different locations between the start point 1002 and theend point 1004 that could be occupied by an AV 100. In some examples,e.g., when the start point 1002 and end point 1004 represent differentmetropolitan areas, the nodes 1006 a-d represent segments of roads. Insome examples, e.g., when the start point 1002 and the end point 1004represent different locations on the same road, the nodes 1006 a-drepresent different positions on that road. In this way, the directedgraph 1000 includes information at varying levels of granularity. In anembodiment, a directed graph having high granularity is also a subgraphof another directed graph having a larger scale. For example, a directedgraph in which the start point 1002 and the end point 1004 are far away(e.g., many miles apart) has most of its information at a lowgranularity and is based on stored data, but also includes some highgranularity information for the portion of the graph that representsphysical locations in the field of view of the AV 100.

The nodes 1006 a-d are distinct from objects 1008 a-b which cannotoverlap with a node. In an embodiment, when granularity is low, theobjects 1008 a-b represent regions that cannot be traversed byautomobile, e.g., areas that have no streets or roads. When granularityis high, the objects 1008 a-b represent physical objects in the field ofview of the AV 100, e.g., other automobiles, pedestrians, or otherentities with which the AV 100 cannot share physical space. In anembodiment, some or all of the objects 1008 a-b are a static objects(e.g., an object that does not change position such as a street lamp orutility pole) or dynamic objects (e.g., an object that is capable ofchanging position such as a pedestrian or other car).

The nodes 1006 a-d are connected by edges 1010 a-c. If two nodes 1006a-b are connected by an edge 1010 a, it is possible for an AV 100 totravel between one node 1006 a and the other node 1006 b, e.g., withouthaving to travel to an intermediate node before arriving at the othernode 1006 b. (When we refer to an AV 100 traveling between nodes, wemean that the AV 100 travels between the two physical positionsrepresented by the respective nodes.) The edges 1010 a-c are oftenbidirectional, in the sense that an AV 100 travels from a first node toa second node, or from the second node to the first node. In anembodiment, edges 1010 a-c are unidirectional, in the sense that an AV100 can travel from a first node to a second node, however the AV 100cannot travel from the second node to the first node. Edges 1010 a-c areunidirectional when they represent, for example, one-way streets,individual lanes of a street, road, or highway, or other features thatcan only be traversed in one direction due to legal or physicalconstraints.

In an embodiment, the planning module 404 uses the directed graph 1000to identify a path 1012 made up of nodes and edges between the startpoint 1002 and end point 1004.

An edge 1010 a-c has an associated cost 1014 a-b. The cost 1014 a-b is avalue that represents the resources that will be expended if the AV 100chooses that edge. A typical resource is time. For example, if one edge1010 a represents a physical distance that is twice that as another edge1010 b, then the associated cost 1014 a of the first edge 1010 a may betwice the associated cost 1014 b of the second edge 1010 b. Otherfactors that affect time include expected traffic, number ofintersections, speed limit, etc. Another typical resource is fueleconomy. Two edges 1010 a-b may represent the same physical distance,but one edge 1010 a may require more fuel than another edge 1010 b,e.g., because of road conditions, expected weather, etc.

When the planning module 404 identifies a path 1012 between the startpoint 1002 and end point 1004, the planning module 404 typically choosesa path optimized for cost, e.g., the path that has the least total costwhen the individual costs of the edges are added together.

Autonomous Vehicle Control

FIG. 11 shows a block diagram 1100 of the inputs and outputs of acontrol module 406 (e.g., as shown in FIG. 4 ). A control moduleoperates in accordance with a controller 1102 which includes, forexample, one or more processors (e.g., one or more computer processorssuch as microprocessors or microcontrollers or both) similar toprocessor 304, short-term and/or long-term data storage (e.g., memoryrandom-access memory or flash memory or both) similar to main memory306, ROM 1308, and storage device 210, and instructions stored in memorythat carry out operations of the controller 1102 when the instructionsare executed (e.g., by the one or more processors).

In an embodiment, the controller 1102 receives data representing adesired output 1104. The desired output 1104 typically includes avelocity, e.g., a speed and a heading. The desired output 1104 can bebased on, for example, data received from a planning module 404 (e.g.,as shown in FIG. 4 ). In accordance with the desired output 1104, thecontroller 1102 produces data usable as a throttle input 1106 and asteering input 1108. The throttle input 1106 represents the magnitude inwhich to engage the throttle (e.g., acceleration control) of an AV 100,e.g., by engaging the steering pedal, or engaging another throttlecontrol, to achieve the desired output 1104. In some examples, thethrottle input 1106 also includes data usable to engage the brake (e.g.,deceleration control) of the AV 100. The steering input 1108 representsa steering angle, e.g., the angle at which the steering control (e.g.,steering wheel, steering angle actuator, or other functionality forcontrolling steering angle) of the AV should be positioned to achievethe desired output 1104.

In an embodiment, the controller 1102 receives feedback that is used inadjusting the inputs provided to the throttle and steering. For example,if the AV 100 encounters a disturbance 1110, such as a hill, themeasured speed 1112 of the AV 100 is lowered below the desired outputspeed. In an embodiment, any measured output 1114 is provided to thecontroller 1102 so that the necessary adjustments are performed, e.g.,based on the differential 1113 between the measured speed and desiredoutput. The measured output 1114 includes measured position 1116,measured velocity 1118, (including speed and heading), measuredacceleration 1120, and other outputs measurable by sensors of the AV100.

In an embodiment, information about the disturbance 1110 is detected inadvance, e.g., by a sensor such as a camera or LiDAR sensor, andprovided to a predictive feedback module 1122. The predictive feedbackmodule 1122 then provides information to the controller 1102 that thecontroller 1102 can use to adjust accordingly. For example, if thesensors of the AV 100 detect (“see”) a hill, this information can beused by the controller 1102 to prepare to engage the throttle at theappropriate time to avoid significant deceleration.

FIG. 12 shows a block diagram 1200 of the inputs, outputs, andcomponents of the controller 1102. The controller 1102 has a speedprofiler 1202 which affects the operation of a throttle/brake controller1204. For example, the speed profiler 1202 instructs the throttle/brakecontroller 1204 to engage acceleration or engage deceleration using thethrottle/brake 1206 depending on, e.g., feedback received by thecontroller 1102 and processed by the speed profiler 1202.

The controller 1102 also has a lateral tracking controller 1208 whichaffects the operation of a steering controller 1210. For example, thelateral tracking controller 1208 instructs the steering controller 1210to adjust the position of the steering angle actuator 1212 depending on,e.g., feedback received by the controller 1102 and processed by thelateral tracking controller 1208.

The controller 1102 receives several inputs used to determine how tocontrol the throttle/brake 1206 and steering angle actuator 1212. Aplanning module 404 provides information used by the controller 1102,for example, to choose a heading when the AV 100 begins operation and todetermine which road segment to traverse when the AV 100 reaches anintersection. A localization module 408 provides information to thecontroller 1102 describing the current location of the AV 100, forexample, so that the controller 1102 can determine if the AV 100 is at alocation expected based on the manner in which the throttle/brake 1206and steering angle actuator 1212 are being controlled. In an embodiment,the controller 1102 receives information from other inputs 1214, e.g.,information received from databases, computer networks, etc.

Active Learning System

FIG. 13 shows an example data sample 1300 that is output by a perceptionmodule 402 that includes 10 bounding boxes 1301 a-1301 k withcorresponding bounding box labels and scores. In the example shown thedata sample is an image captured by a video camera. The bounding boxlabels identify the object and the bounding box scores indicate a levelof confidence that its corresponding bounding box includes a correctlylabeled object. In any giving data sample, there can be bounding boxesthat include an object, bounding boxes that do not include an object andbounding boxes that may include an object. In the data sample 1300, thebounding box scores range from 0.00-1.00, where 0.00 means no objectdetected, 1.00 means an object was detected and correctly labeled andthe numbers in between represent a degree of confidence that an objectwas detected and correctly labeled. For example, bounding box 1301 ccontains a car and has a bounding box score of 1.00, indicating that thecar was detected and correctly labeled as a “car.” Likewise, boundingbox 1301 d includes a pedestrian and has a bounding box score of 0.96,indicating a high confidence that the pedestrian was correctly labeled a“person.” Bounding box 1301 k contains no object and thus has a boundingbox score of 0.00. Note that in practice each bounding box has abounding box score for each class. However, after various filteringsteps only bounding boxes with high scores (higher than a specifiedthreshold) are included in the output of the perception module.

Thousands of data samples with bounding boxes and bounding box scorescan be collected from perception modules in AVs and stored in adatabase. To reduce the expense and time for manual labeling, an activelearning system is used to automatically query a human annotator onwhich unlabeled bounding boxes are to be labeled, or incorrectly labeledbounding boxes are to be re-labeled, as described below in reference toFIG. 14 . In an embodiment, the active learning system is implemented ina perception module 402 of an AV 100, and the selected data samples areprovided by the AV 100 computer system 300 to the human annotatorthrough a wireless transmission or other transfer mechanism (e.g., thumbdrive). In other embodiments, the AV 100 provides the collected datasamples to the human annotator, and an active learning applicationinstalled on a computer system remote from the AV 100 is used toautomatically select data samples to annotate.

Since there is too much sensor data to store in a practical system, theactive learning system disclosed herein helps reduce the amount of datasamples stored in the database by identifying erroneous annotations inthe database, and to find a balance between quantity and quality of datasamples in the database to optimize system performance and cost.Moreover, the effects of different kinds of annotation errors (e.g.,missing objects, inaccurate position) on performance can be analyzed, aswell as training the same neural network with different amounts of data.

FIG. 14 shows a block diagram of an active learning system 1400 forautomatically selecting a subset of data samples 1401 for annotation. Inthe example shown, active learning system 1400 includes data samples1401, bounding box prediction module 1402, spatial probability densitymodule 1403, ensemble score variance module 1404, selected data samples1405 and annotator 1406.

There are scenarios in which unlabeled or incorrectly labeled data isabundant but manual labeling by a human is expensive. In such scenarios,the active learning system 1400 automatically queries the annotator 1406for labels. Data samples 1401 (e.g., images, point clouds) includebounding boxes with known labels and bounding boxes with unknown labels.The labeled bounding boxes can be correctly labeled or incorrectlylabeled. The active learning system 1400 automatically selects a subsetof bounding boxes with unknown and/or incorrect labels for manuallabeling (annotating) by the annotator 1406.

More particularly, in a first step the bounding box prediction module1402 retrieves a set of labeled bounding boxes and their correspondingbounding box scores from the input data samples 1401. The bounding boxprediction module 1402 then generates a probability distribution ofbounding box scores over the set of labeled bounding boxes. In anembodiment, the probability distribution is approximated by a stackedhistogram, as shown in FIG. 15 .

FIG. 15 shows a stacked histogram of bounding box scores. For eachlabel, a stacked histogram is generated based on a number of correctlyand incorrectly labeled bounding boxes in the set of labeled boundingboxes. In this example, the stacked histogram is for a “pedestrian”label. The x-axis includes the bins of the histogram that representranges of bounding box scores (0.0-1.0). The y-axis is the number ofbounding boxes in each bin. Each bin has a number 1501 of incorrectlylabeled bounding boxes and a number 1502 of correctly labeled boundingboxes. These numbers depend on an optimal confidence threshold 1503 anda ground-truth label. The optimal confidence threshold 1503 weighs arelationship between false positives (too many detections) and falsenegatives (too many missing detections). For example, bounding boxeswith scores that fall to the left of the optimal confidence threshold1503 are deemed to have incorrect labels, and bounding boxes with scoresthat fall to the right of the optimal confidence threshold 1503 aredeemed to have correct labels.

The likelihood 1504 that a given bounding box score is incorrectlyclassified is determined by the ratio of the number of incorrectlylabeled bounding boxes 1501 and the sum of the number of incorrectlylabeled bounding boxes 1501 and the number of correctly labeled boundingboxes 1502. As can be observed from FIG. 15 , there is a peak 1505 inlikelihood 1504 at close to the optimal confidence threshold 1503 of thepedestrian label. When selecting unlabeled bounding boxes forannotation, the active learning system 1400 will select bounding boxeshaving bounding box scores around the peak 1505.

Referring back to FIG. 14 , the next step performed by the activelearning system 1400 is to compute a spatial probability density over aset of bounding boxes for each label, sensor and scale. Since most ofthe data samples are collected from an AV while driving level, andbecause the sensors (e.g., camera, LiDAR, RADAR) are usually fixed inposition, the spatial probability density can be used to identifyoutlier bounding boxes that would not be suitable for annotation.

In an embodiment, for each label, sensor (e.g., a camera) and scale, aGaussian Mixture Model (GMM) is applied to the set of bounding boxes.The GMM is parameterized by bounding box size (width, height) and thebounding box position (x, y) in the sample reference frame, where x, ycan be the position coordinates of the upper left-hand corner of thebounding box in the sample reference frame (e.g., image referenceframe). To treat the spatial densities as probabilities, the spatialdensities are normalized to be in a range of [0, 1] by dividing thespatial densities by the largest spatial density value encountered inthe set of bounding boxes. In an embodiment, a minimum value for thelargest spatial density value can be enforced (e.g., 1e-4) to avoiddivide by zero errors resulting from the normalizing.

Referring again to FIG. 16 , the plot shows the spatial probabilitydensity of the GMM for the “bike_without_rider” label and camera BO fora bounding box scale of medium size (e.g., a box factor of 1.0). In thisexample, the camera BO faces backwards which is why the spatialprobability density is symmetric along the vertical axis. If the camerafaced to side of the AV 100, the spatial probability density may not besymmetrical.

The spatial probability density shows where a bike without a rider wouldlikely be found in the image. The plot can be viewed as a probabilitydistribution of bikes without riders over the image space. It can beobserved from the plot that most of the densities are clustered in amiddle band 1600 and that there are likelihood peaks that extend intothe lower left and rights side of the plot. When selecting boundingboxes for annotation, the active learning system 1400 will selectbounding boxes in the middle band 1600. Bounding boxes in the upperportion of the plot and in the lower portion of the plot are likely tobe outliers. For example, since the upper portion represents the sky inthe image (assuming AV is level), it would be unlikely to detect a bikein the sky. Accordingly, bounding boxes that lie outside the middle band1600 are be excluded for annotation.

Referring again to FIG. 14 , the next step performed by the activelearning system 1400 is for the ensemble score variance module 1404 tocompute an ensemble score variance. As shown in FIG. 17 , N machinelearning (ML) models 1701 a-1701 n (e.g., 3 neural networks) are trainedwith training data (e.g., training images). In an embodiment, a singlebase neural network is trained, N copies of the base neural network arecreated and each copy is fine-tuned using a different random order ofthe training data. The “ensemble” of machine learning models (e.g.,CNNs) generates N sets of predictions which are fed into a pairwisecomparator module 1702. The pairwise comparator module 1702 computes thepairwise agreement between the N sets of predictions to generate Nensemble scores (e.g., set of N mean average precision (mAP) values),where each set of predictions, in turn, is the “ground truth” for theother sets of predictions on a box level. Ensemble score variancegenerator 1703 then computes an ensemble score variance which is ameasure of the difference between the N ensemble scores. When selectingdata samples (e.g., images) for annotation, the active learning system1400 selects data samples having the highest ensemble score variance(highest uncertainty). In an alternative embodiment, cross-modalensembles are used where a measure of agreement is made between objectdetections output by multiple different sensors (e.g., RADAR, LiDAR andcamera).

In an embodiment, temporal inconsistency cues are used to determine ifsuccessive frames of data samples (e.g., successive video frames)contain temporal noise (e.g., “flicker”). For example, an object maydisappear and then reappear in successive video frames. Object trackingalgorithms (e.g., contour tracking, kernel-based tracking, CNN-basedapproaches) can be used to track the objects across frames to detect“flicker.” The data samples that cause flicker are selected forannotation.

In an embodiment, digital maps are used to determine if objects areviolating map constraints. For example, a car that is detected inside abuilding violates a map constraint. Bounding boxes that violate mapconstraints are excluded from selection for annotation. The mapconstraints do not need to be hard constraints. In an embodiment, thelikelihood that a pedestrian is on a road, sidewalk, etc., isstatistically modeled.

FIG. 18 shows a flow diagram of an active learning process forautomatically selecting data samples for annotation. Process 1800 can beimplemented by the active learning system shown in FIG. 14 using, forexample, the AV computer system 300 shown in FIG. 3 .

Process 1800 begins by obtaining data samples that include boundingboxes and corresponding bounding box scores (1801). For example, datasamples can be generated by an object detector in a perception module ofan AV, as described in reference to FIGS. 4 and 5 . The object detectorcan be a neural network (e.g., a CNN). The object detector can take asinput images and/or point clouds captured by a camera and LiDAR/RADAR,respectively. The object detector outputs the image or point cloud withbounding boxes surrounding the detected objects, labels labeling theobjects and bounding box scores indicating the confidence of the objectdetector that the bounding boxes were labeled correctly.

Process 1800 continues by generating a distribution of bounding boxscores (1802). For example, the process 1800 is capable of identifyingbounding boxes having bounding box scores on the left of the optimalconfidence threshold 1503 (FIG. 15 ) as being labeled incorrectly andbounding boxes having bounding box scores on the right side of theoptimal confidence threshold 1503 as being labeled correctly. A stackedhistogram can then be created to approximate the distribution using thenumber of incorrectly and correctly labeled bounding boxes, as describedin reference to FIG. 15 . A likelihood is computed for each bin of thestacked histogram and used to select bounding boxes for annotation.

Process 1800 continues by generating a spatial probability densityacross a set of bounding boxes for each label, sensor and scale based onbox size and position (1803). For example, a GMM parameterized by boxsize and position in an image sample can be used to generate the spatialprobability density for the set of bounding boxes. The spatialprobability density is used to determine outlier bounding boxes toselect for annotation, as described in reference to FIG. 16 .

Process 1800 continues by generating an ensemble score variance fromsets of predictions output from multiple machine learning models (1804).For example, N machine learning (ML) models (e.g., 3 CNNs) are trainedwith training data (e.g., training images). The “ensemble” of machinelearning models generates N sets of predictions which are fed into apairwise comparator module. The pairwise comparator module computes thepairwise agreement between the N sets of predictions to generate Nensemble scores (e.g., set of N mAP values), where each set ofpredictions, in turn, is the “ground truth” for the other sets ofpredictions on a box level. An ensemble score variance generator thencomputes an ensemble score variance which is a measure of the differencebetween the N ensemble scores. When selecting bounding boxes forannotation, the active learning system will select data samples with thehighest variance. In an alternative embodiment, cross-modal ensemblesare used where a measure of agreement is made between object detectionsoutput by multiple different sensors (e.g., RADAR, LiDAR and camera).

Process 1800 continues by using particular combinations of bounding boxscore distribution, spatial probability density and ensemble scorevariance to select data samples for annotation (1805). In an embodiment,the ensemble score variance and the distribution of bounding box scoresare used to select data samples for annotation without using the spatialprobability density. In an embodiment, the ensemble score variance andspatial probability density are used to select data samples forannotation without using the distribution of bounding box scores. In anembodiment, the distribution of bounding box scores and the spatialprobability density are used to select data samples for annotationwithout using the ensemble score variance.

In the foregoing description, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. The description and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense. The sole and exclusive indicator of the scope of the invention,and what is intended by the applicants to be the scope of the invention,is the literal and equivalent scope of the set of claims that issue fromthis application, in the specific form in which such claims issue,including any subsequent correction. Any definitions expressly set forthherein for terms contained in such claims shall govern the meaning ofsuch terms as used in the claims. In addition, when we use the term“further comprising,” in the foregoing description or following claims,what follows this phrase can be an additional step or entity, or asub-step/sub-entity of a previously-recited step or entity.

What is claimed is:
 1. A method comprising: obtaining, using one or moreprocessors, a set of data samples, each data sample including one ormore bounding boxes, each bounding box containing a potential object orscene in an environment, each bounding box having a label and boundingbox score indicating a confidence in the label being correct; andselecting, using the one or more processors, a subset of data samplesfor annotation based on a bounding box prediction confidence determinedusing a probability distribution of bounding box scores, and an ensemblescore variance based on differences in ensemble scores computed fromsets of predictions output by multiple machine learning models.
 2. Themethod of claim 1, further comprising: selecting, using the one or moreprocessors, the subset of data samples for annotation based on thebounding box prediction confidence, a spatial probability density of thebounding boxes parameterized by bounding box size and position and theensemble score variance.
 3. The method of claim 1, wherein the boundingbox prediction further comprises: for each label: generating aprobability distribution of bounding box scores; and determining, basedon the distribution, a likelihood that a particular bounding box isincorrectly labeled; and selecting or excluding the particular boundingbox for annotation based on the likelihood.
 4. The method of claim 3,wherein the distribution is approximated by a histogram having binsrepresenting ranges of bounding box scores, and each bin is associatedwith a likelihood.
 5. The method claim 4, wherein for each bin thelikelihood is calculated from a ratio of a number of incorrectly labeledbounding boxes assigned to the bin and a sum of the number ofincorrectly labeled bounding boxes and a number of labeled boundingboxes assigned to the bin.
 6. The method of claim 2, further comprising:for each label, sensor and scale: determining the spatial probabilitydensity using a Gaussian Mixture Model (GMM) over a set of boundingboxes for the label, sensor and scale, where the GMM is parameterized bybounding box size and position.
 7. The method of claim 6, wherein thespatial probability density for the label is determined by dividing thespatial densities for the label by a largest density value among allspatial density values for the label.
 8. The method of claim 1, furthercomprising: processing the data samples through a plurality of differentmachine learning models to generate predicted labeled bounding boxes;computing an ensemble score for each pairwise comparison of thepredicted labeled bounding boxes, where each predicted labeled boundingbox is a ground truth for comparison with the other predicted labeledbounding boxes; and computing an ensemble score variance based on theensemble scores.
 9. The method of claim 8, wherein the plurality ofdifferent machine learning models includes a plurality of differentneural networks tuned by training data samples provided by differenttypes of sensors.
 10. The method of claim 9, wherein the different typesof sensors include LiDAR, RADAR and a camera.
 11. The method of claim 9,wherein the plurality of different neural networks are trained ondifferent random orders of the training data samples.
 12. The method ofclaim 1, further comprising: detecting, by the one or more processors,temporal inconsistency between successive data samples; in accordancewith temporal inconsistency being detected, selecting at least one ofthe successive data samples for annotation.
 13. The method of claim 1,further comprising: using, by the one or more processors, mapconstraints to detect an error associated with a bounding box; and inaccordance with the error being detected, excluding the bounding boxfrom annotation.
 14. An active learning system, comprising: a storagedevice including data samples; one or more processors; memory storinginstructions that when executed by the one or more processors, cause theone or more processors to perform operations comprising: obtaining a setof data samples, each data sample including one or more bounding boxes,each bounding box containing a potential object or scene in anenvironment, each bounding box having a label and bounding box scoreindicating a confidence in the label being correct; and selecting asubset of data samples for annotation based on a bounding box predictionconfidence determined using a probability distribution of bounding boxscores, and an ensemble score variance based on differences in ensemblescores computed from sets of predictions output by multiple machinelearning models.
 15. The system of claim 14, further comprising:selecting the subset of data samples for annotation based on thebounding box prediction confidence, a spatial probability density of thebounding boxes parameterized by bounding box size and position and theensemble score variance.
 16. The system of claim 14, wherein thebounding box prediction further comprises: for each label: generating aprobability distribution of bounding box scores; and determining, basedon the distribution, a likelihood that a particular bounding box isincorrectly labeled; and selecting or excluding the particular boundingbox for annotation based on the likelihood.
 17. The system of claim 16,wherein the distribution is approximated by a histogram having binsrepresenting ranges of bounding box scores, and each bin is associatedwith a likelihood.
 18. The system claim 17, wherein for each bin thelikelihood is calculated from a ratio of a number of incorrectly labeledbounding boxes assigned to the bin and a sum of the number ofincorrectly labeled bounding boxes and a number of labeled boundingboxes assigned to the bin.
 19. The system of claim 15, furthercomprising: for each label, sensor and scale: determining the spatialprobability density using a Gaussian Mixture Model (GMM) over a set ofbounding boxes for the label, sensor and scale, where the GMM isparameterized by bounding box size and position.
 20. The system of claim19, wherein the spatial probability density for the label is determinedby dividing the spatial densities for the label by a largest densityvalue among all spatial density values for the label.
 21. The system ofclaim 14, further comprising: processing the data samples through aplurality of different machine learning models to generate predictedlabeled bounding boxes; computing an ensemble score for each pairwisecomparison of the predicted labeled bounding boxes, where each predictedlabeled bounding box is a ground truth for comparison with the otherpredicted labeled bounding boxes; and computing an ensemble scorevariance based on the ensemble scores.
 22. The system of claim 21,wherein the plurality of different machine learning models includes aplurality of different neural networks tuned by training data samplesprovided by different types of sensors.
 23. The system of claim 22,wherein the different types of sensors include LiDAR, RADAR and acamera.
 24. The system of claim 22, wherein the plurality of differentneural networks are trained on different random orders of the trainingdata samples.
 25. The system of claim 14, further comprising: detectingtemporal inconsistency between successive data samples; in accordancewith temporal inconsistency being detected, selecting at least one ofthe successive data samples for annotation.
 26. The system of claim 14,further comprising: using map constraints to detect an error associatedwith a bounding box; and in accordance with the error being detected,excluding the bounding box from annotation.